Gender: Male 
Age: 45-54
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Single 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Graduate degree
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 2
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Father's occupation: Plant and machine operators, and assemblers (those who maintain, operate and monitor industrial and agricultural machinery and equipment)
Hobbies: Listen to rock/indie music;Exercise;Use social media;Go to museums and galleries;Watch sports 
Devices used daily: Laptop;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative) 
Frequency of LLM use: Sometimes
LLMs used: Claude;Google Bard;Microsoft Bing AI 
LLM use cases: Translation;Generic chatbot conversation;Solving math or logical problems;Analyze data;Learning (e.g., explaining complex concepts in simple terms);Get information about current events;Answering questions about general knowledge 
Contexts of LLM use: Entertainment;Learning;Personal finding answers while teaching 
Prompt 1: Does the Pavallion Restaurant in Vermillion, Ohio take credit cards?
Prompt 2: What are the Columbus Blue Jackets chances of winning the Stanley Cup?
Prompt 3: What are the Columbus Blue Jackets chances of making the playoffs?
Prompt 4: international falls Minnesota 1 pound package 121212 shipping rates
Prompt 5: Can you print shipping labels off of eBay and Amazon seller accounts?
Prompt 6: I want to know the odds of the Columbus Blue Jackets winning the Stanley Cup?
Prompt 7: Driving distance Rocky River, Ohio to Atlantic City
Prompt 8: driving Distance New York City to Atlantic City
Prompt 9: Is Atlantic City safe?
Prompt 10: Does Atlantic City still have boardwalk cats?
Comments: I often use AI for math. I teach and I often need quick answers when working with students. Sometimes a student will bring up their Chromebook (laptop) and ask "Is this right?"